Predictive search context system for targeted recommendations

Abstract

This disclosure describes techniques for analyzing search metadata associated with a client-initiated search performed via an internet search engine. Particularly, a Predictive Search Context (PSC) System is described that may analyze search metadata relative to client behavior data to provide a client with one or more recommendations. The recommendations may relate to an event, merchant, place, product, service, and/or category thereof. Further, the PSC system may use client behavior data (i.e., client behavior model) associated with a client, to predict a next, or near to next, probable location of the client. In this example, the PSC system may generate client behavior data based on client-initiated searches performed on client devices operated exclusively or non-exclusively by the client. In doing so, the PSC system may analyze search metadata associated with one of the client devices to identify the client and determine a next, or near to next probable location of the client.

What is claimed: 
     
         1 . A system comprising:
 one or more processors;   memory coupled to the one or more processors, the memory including one or more modules that are executable by the one or more processors:   retrieve search metadata of a client-initiated search that is conducted via an internet search engine, the client-initiated search occurring on a client device operating on a telecommunications network, wherein the search metadata includes a number of bits associated with a character string of the client-initiated search and an Internet Protocol (IP) address that corresponds to a subsequent internet search result;   parse through the search metadata to determine a search context that corresponds to the client-initiated search;   retrieve, from a data store, one or more recommendations for presentation to the client device, based at least in part on the search context; and   select at least one recommendation of the one or more recommendations to present to the client device.   
     
     
         2 . The system of  claim 1 , wherein the one or more modules are further executable by the one or more processors to:
 retrieve, from a data store, client behavior data associated with the client device, the client behavior data including instances of historical search metadata and corresponding search contexts; and   generate a client behavior model based at least in part on the client behavior data, the client behavior model to identify at least one recommendation to present to the client device;   analyze the client behavior model to identify data patterns between the search context and the client behavior data, and   wherein to select the at least one recommendation is based at least in part on an analysis of the client behavior model.   
     
     
         3 . The system of  claim 1 , wherein the search metadata further includes a time-stamp associated with the client-initiated search, and
 wherein, to select the at least one recommendation is based at least in part on a time of day or day of a week that corresponds to the time-stamp associated with the client-initiated search.   
     
     
         4 . The system of  claim 3 , wherein the one or more modules are further executable by the one or more processors to:
 retrieve, from the client device, a device identifier associated with the client device; and   determine a geographic location of the client device at a point in time that corresponds to a time-stamp of the client-initiated search, based at least in part on the device identifier, and   wherein to select the at least one recommendation is further based at least in part on the geographic location of the client device.   
     
     
         5 . The system of  claim 1 , wherein the one or more modules are further executable by the one or more processors to:
 retrieve, from a data store, client behavior data associated with the client device; and   analyze the client behavior data to identify data patterns between the one or more recommendations and the client behavior data, and   wherein to select the at least one recommendation is based at least in part on an analysis of the client behavior data.   
     
     
         6 . The system of  claim 5 , wherein the one or more modules are further executable by the one or more processors to:
 assign a suitability score to individual recommendations of the one or more recommendations, the suitability score being based on the data patterns between the individual recommendations and the client behavior data, and   wherein, to select the at least one recommendation is further based at least in part on the suitability score being greater than a predetermined suitability threshold.   
     
     
         7 . The system of  claim 1 , wherein the one or more modules are further executable by the one or more processors to:
 retrieve, from the client device, a device identifier associated with the client device; and   identify the internet search engine based at least in part on the device identifier, and   wherein, to parse through the search metadata to determine a search context is based at least in part on an identity of the internet search engine.   
     
     
         8 . The system of  claim 1 , wherein the one or more modules are further executable by the one or more processors to:
 retrieve, from a data store, client behavior data associated with the client device, the client behavior data including instances of historical search metadata associated with the client device and instances of historical search contexts that correspond to the instances of historical search metadata, and wherein the one or more modules are further executable by the one or more processors to:   determine a similarity of the search metadata and instances of historical search metadata, and   wherein to determine the search context is based at least in part on a similarity of the search metadata and one instance of the instances of historical search metadata being greater than a predetermined similarity threshold.   
     
     
         9 . The system of  claim 1 , wherein the one or more modules are further executable by the one or more processors to:
 determine a number of characters associated with the client-initiated search, based at least in part on the number of bits associated with the client-initiated search, and   wherein to determine the search context is further based at least in part on the number of characters.   
     
     
         10 . A computer-implemented method, comprising:
 under control of one or more processors:   retrieving, from a client device, search metadata of a client-initiated search that is conducted via an internet search engine, the search metadata including a time-stamp associated with the client-initiated search, a device identifier associated with the client device, and an Internet Protocol (IP) address that corresponds to an internet search result;   parsing through the search metadata to determine a search context of the client-initiated search, the search context corresponding to one of an event, a category of events, a merchant, a category of merchants, a place, or a category of places;   retrieving, from a data store, one or more recommendations for presentation to the client device, based at least in part on the search context;   generating a client behavior model to select at least one recommendation to present to the client device, the client behavior model being based at least in part on client behavior data associated with the client device;   analyzing the client behavior model to identify data patterns between the search context and the client behavior data; and   selecting at least one recommendation for presentation to the client device, based at least in part on an analysis of the client behavior model.   
     
     
         11 . The computer-implemented method of  claim 10 , further comprising:
 determining a geographic location of the client device, based at least in part on the device identifier associated with the client device, and   wherein selecting the at least one recommendation for presentation to the client device is further based at least in part on the geographic location of the client device.   
     
     
         12 . The computer-implemented method of  claim 10 , further comprising:
 retrieving, from a data store, the client behavior data associated with the client device, the client behavior data including instances of historical search metadata and corresponding instances of historical search contexts, and   wherein parsing through the search metadata to determine the search context is further based at least in part on the client behavior data.   
     
     
         13 . The computer-implemented method of  claim 10 , further comprising:
 generating modified client behavior data by adding the search metadata associated with the client-initiated search to the client behavior data; and   updating the client behavior model based at least in part on the modified client behavior data.   
     
     
         14 . The computer-implemented method of  claim 10 , further comprising:
 retrieving, from a data store, one or more recommendations for presentation to the client device, based at least in part on the search context; and   determining a suitability score for individual recommendations of the one or more recommendations, based at least in part on the analysis of the client behavior model, and   wherein, selecting the at least one recommendation is further based at least in part on suitability score of the at least one recommendation being greater than a predetermined suitability threshold.   
     
     
         15 . The computer-implemented method of  claim 10 , further comprising:
 retrieving, from a data store, client behavior data associated with the client device, the client behavior data including instances of historical search metadata and corresponding instances of historical search context; and   determining a similarity of the search metadata with the instances of historical search metadata, and   wherein parsing through the search metadata to determine the search context is based at least in part on the similarity being greater than a predetermined similarity threshold.   
     
     
         16 . One or more non-transitory computer-readable media storing computer executable instructions that, when executed on one or more processors, cause the one or more processors to perform acts comprising:
 retrieving, from a client device, search metadata of a client-initiated search that is conducted via an internet search engine, the search metadata including a device identifier associated with the client device, a time-stamp associated with the client-initiated search, and IP address that corresponds to an internet search results;   determining, a search context that corresponds to the client-initiated search, based at least in part on the search metadata, the search context including at least one of an event, a category of events, a merchant, a category of merchants, a place, or a category of places; and   selecting a recommendation to present to the client device, based at least in part on the search context.   
     
     
         17 . The one or more non-transitory computer-readable media of  claim 16 , further storing instructions that, when executed cause the one or more processors to perform acts comprising:
 retrieving, from a data store, client behavior data associated with the client device;   generating a client behavior model to identify one or more recommendations to present to the client device, based at least in part on the search metadata, the client behavior model including client behavior data over a predetermined time interval; and   analyzing the client behavior model to identify data patterns between the search context and the client behavior data, and   wherein, selecting the recommendation is further based at least in part on an analysis of the client behavior model.   
     
     
         18 . The one or more non-transitory computer-readable media of  claim 17 , further comprising:
 generating modified client behavior data by adding to the client behavior data the search metadata associated with the client-initiated search and the search context that corresponds to the search metadata;   removing a portion of client behavior data from the modified client behavior data that is associated with a time-stamp beyond the predetermined time interval; and   updating the client behavior model, based at least in part on the modified client behavior data.   
     
     
         19 . The one or more non-transitory computer-readable media of  claim 16 , further comprising:
 determining a geographic location of the client device at a point of time that corresponds to the time-stamp of the client-initiated search, and   wherein, determining the search context is based at least in part on the geographic location of the client device.   
     
     
         20 . The one or more non-transitory computer-readable media of  claim 16 , wherein the search metadata includes a plurality of subsequent IP addresses accessed by the client device within a predetermined time interval of the time-stamp of the client-initiated search, and
 wherein, determining the search context is further based at least in part on the plurality of subsequent IP addresses.